We use the same kernel widths, strides, and feature map configurations as in the WSEGAN work [1]}. These are kernels of width 31 in all convolutional layers for both \(G\)  and \(D\) . The feature maps are incremental in the encoder and decremental in the decoder, having \(\lbrace 64, 128, 256, 512, 1024, 512, 256, 128, 64, 1\rbrace \)  in \(G\)  and \(\lbrace 64, 128, 256, 512, 1024\rbrace \)  in \(D\) . The discriminator is then the one diverging from its predecessors in earlier works [2]}, [1]}. Firstly, it has a multi-layer perceptron (MLP) with 16384 inputs (1024\(\times \) 16 feature map, unrolled), 256 hidden PReLU units, and the single output for real and fake predictions. Secondly, we find the acoustic branch for SEGAN-Aco and SEGAN-PTAco models at the fourth convolutional layer, which outputs 512\(\times \) 64 feature maps for an input of approximately 1 s at 16 kHz sampling rate. These corresponds to 64 frames, each one injected into the acoustic prediction MLP of 128 hidden PReLU units and 277 linear outputs. These outputs predict 257 log-power spectral bins, 16 MFCCs, 1 log-F0 value of the frame, 1 voiced/unvoiced frame flag, 1 frame energy coefficient, and 1 frame zero crossing rate. Mini-batches of 150 samples are used for all models. Additionally, \(D\)  implements spectral normalization to avoid sudden exploding gradients leading to training collapse [4]}, [5]} and phase shift of 5 to reduce high-frequency artifacts in the output of \(G\)  [6]}. All the experiments were developed with the public SEGAN framework at https://github.com/santi-pdp/segan_pytorch.
